OpenRaiser/Intern-Atlas huggingface dataset:
Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentations
Venue: arXiv cs.CV 2019
Year: 2019

Objective: Overlapping measures are often utilized to quantify the similarity between two binary regions. However, modern segmentation algorithms output a probability or confidence map with continuous values in the zero-to-one interval. Moreover, these binary overlapping measures are biased to structure size. Addressing these challenges is the objective of this work. Methods: We extend the definition of the classical Dice coefficient (DC) overlap to facilitate the direct comparison of a ground truth binary image with a probabilistic map. We call the extended method continuous Dice coefficient (cDC) and show that 1) cDC is less or equal to 1 and cDC = 1 if-and-only-if the structures overlap is complete, and, 2) cDC is monotonically decreasing with the amount of overlap. We compare the classical DC and the cDC in a simulation of partial volume effects that incorporates segmentations of common targets for deep-brainstimulation. Lastly, we investigate the cDC for an automatic segmentation of the subthalamic-nucleus. Results: Partial volume effect simulation on thalamus (large structure) resulted with DC and cDC averages (SD) of 0.98 (0.006) and 0.99 (0.001), respectively. For subthalamic-nucleus (small structure) DC and cDC were 0.86 (0.025) and 0.97 (0.006), respectively. The DC and cDC for automatic STN segmentation were 0.66 and 0.80, respectively. Conclusion: The cDC is well defined for probabilistic segmentation, less biased to structure size and more robust to partial volume effects in comparison to DC. Significance: The proposed method facilitates a better evaluation of segmentation algorithms. As a better measurement tool, it opens the door for the development of better segmentation methods.
Smart Contract Federated Identity Management without Third Party Authentication Services
Venue: arXiv cs.CR 2019
Year: 2019

Federated identity management enables users to access multiple systems using a single login credential. However, to achieve this a complex privacy compromising authentication has to occur between the user, relying party (RP) (e.g., a business), and a credential service provider (CSP) that performs the authentication. In this work, we use a smart contract on a blockchain to enable an architecture where authentication no longer involves the CSP. Authentication is performed solely through user to RP communications (eliminating fees and enhancing privacy). No third party needs to be contacted, not even the smart contract. No public key infrastructure (PKI) needs to be maintained. And no revocation lists need to be checked. In contrast to competing smart contract approaches, ours is hierarchically managed (like a PKI) enabling better validation of attribute providers and making it more useful for large entities to provide identity services for their constituents (e.g., a government) while still enabling users to maintain a level of self-sovereignty.
Reasoning about Hypothetical Agent Behaviours and their Parameters
Venue: arXiv cs.MA 2019
Year: 2019

Agents can achieve effective interaction with previously unknown other agents by maintaining beliefs over a set of hypothetical behaviours, or types, that these agents may have. A current limitation in this method is that it does not recognise parameters within type specifications, because types are viewed as blackbox mappings from interaction histories to probability distributions over actions. In this work, we propose a general method which allows an agent to reason about both the relative likelihood of types and the values of any bounded continuous parameters within types. The method maintains individual parameter estimates for each type and selectively updates the estimates for some types after each observation. We propose different methods for the selection of types and the estimation of parameter values. The proposed methods are evaluated in detailed experiments, showing that updating the parameter estimates of a single type after each observation can be sufficient to achieve good performance.
Turing Test Revisited: A Framework for an Alternative
Venue: arXiv cs.AI 2019
Year: 2019

This paper aims to question the suitability of the Turing Test, for testing machine intelligence, in the light of advances made in the last 60 years in science, medicine, and philosophy of mind. While the main concept of the test may seem sound and valid, a detailed analysis of what is required to pass the test highlights a significant flow. Once the analysis of the test is presented, a systematic approach is followed in analysing what is needed to devise a test or tests for intelligent machines. The paper presents a plausible generic framework based on categories of factors implied by subjective perception of intelligence. An evaluative discussion concludes the paper highlighting some of the unaddressed issues within this generic framework.
MagneticSpy: Exploiting Magnetometer in Mobile Devices for Website and Application Fingerprinting
Venue: arXiv cs.CR 2019
Year: 2019

Recent studies have shown that aggregate CPU usage and power consumption traces on smartphones can leak information about applications running on the system or websites visited. In response, access to such data has been blocked for mobile applications starting from Android 8. In this work, we explore a new source of side-channel leakage for this class of attacks. Our method is based on the fact that electromagnetic activity caused by mobile processors leads to noticeable disturbances in magnetic sensor measurements on mobile devices, with the amplitude being proportional to the CPU workload. Therefore, recorded sensor data can be analyzed to reveal information about ongoing activities. The attack works on a number of devices: we evaluated 80 models of modern smartphones and tablets and observed the reaction of the magnetometer to the CPU activity on 56 of them. On selected devices we were able to successfully identify which application has been opened (with up to 90% accuracy) or which web page has been loaded (up to 91% accuracy). The presented side channel poses a significant risk to end users' privacy, as the sensor data can be recorded from native apps or even from web pages without user permissions. Finally, we discuss possible countermeasures to prevent the presented information leakage.
What concerns do Chinese parents have about their children's digital adoption and how to better support them?
Venue: arXiv cs.HC 2019
Year: 2019

Digital devices are widely used by children, and children nowadays are spending more time online than with other media sources, such as watching television or playing offline video games. In the UK, 44% of children aged five to ten have been provided with their own tablets, with this percentage increasing annually, while in the US, ownership of tablets by children in this age group grew fivefold between 2011 and 2013. Our previous research found that UK children and parents need better support in dealing with online privacy risks. Interestingly, very few research was done on Chinese children and parents. In this report, we present findings from our online survey of 593 Chinese parents with children aged 6-10 in February and March 2019. Our study particularly focused on understanding Chinese parents' awareness and management of their children's online privacy risks. The goal of the survey was to examine the current adoption pattern of digital devices by Chinese families with young children, the concerns Chinese parents have about their children's online activities and the current practices they use for safeguarding their children online. Our findings imply that we need to continue presenting specific guidance to parents in order to support their choice of digital content for their young children. Further, we need to look more deeply into the roles schools are taking in children's online activities, how can we support schools and teachers when they are making recommendations to parents and children.
FPGA-based Multi-Chip Module for High-Performance Computing
Venue: arXiv cs.AR 2019
Year: 2019

Current integration, architectural design and manufacturing technologies are not suited for the computing density and power efficiency requested by Exascale computing. New approaches in hardware architecture are thus needed to overcome the technological barriers preventing the transition to the Exascale era. In that scope, we report successful fabrication of first ExaNoDe's MCM prototypes dedicated to Exascale computing applications. Each MCM was composed of 2 Xilinx Zynq Ultrascale+ MPSoC, assembled on advanced 68.5 mm x 55 mm laminate substrates specifically designed and fabricated for the project. Acoustic microscopy, x-ray, cross-section and Thermo-Moire investigations revealed no voids, shorts, delamination, cracks or warpage issues. Two MCMs were mounted on a daughter board by FORTH for testing purposes. The DDR memories on the 4 SODIMMs of the daughter board were successfully tested by running extensive Xilinx memory tests with clock frequencies of 1866 MHz and 2133 MHz. All 4 FPGAs were programmed with the Xilinx integrated bit error ratio test (IBERT) tailored for this board for links testing. All intra-board high-speed links between all FPGAs were stable at 10 Gbps, even under the more demanding 31-bit PRBS (Pseudorandom Binary Sequence) tests.
Deployable probabilistic programming
Venue: arXiv cs.PL 2019
Year: 2019

We propose design guidelines for a probabilistic programming facility suitable for deployment as a part of a production software system. As a reference implementation, we introduce Infergo, a probabilistic programming facility for Go, a modern programming language of choice for server-side software development. We argue that a similar probabilistic programming facility can be added to most modern general-purpose programming languages. Probabilistic programming enables automatic tuning of program parameters and algorithmic decision making through probabilistic inference based on the data. To facilitate addition of probabilistic programming capabilities to other programming languages, we share implementation choices and techniques employed in development of Infergo. We illustrate applicability of Infergo to various use cases on case studies, and evaluate Infergo's performance on several benchmarks, comparing Infergo to dedicated inference-centric probabilistic programming frameworks.
Re-ranking Based Diversification: A Unifying View
Venue: arXiv cs.IR 2019
Year: 2019

We analyze different re-ranking algorithms for diversification and show that majority of them are based on maximizing submodular/modular functions from the class of parameterized concave/linear over modular functions. We study the optimality of such algorithms in terms of the `total curvature'. We also show that by adjusting the hyperparameter of the concave/linear composition to trade-off relevance and diversity, if any, one is in fact tuning the `total curvature' of the function for relevance-diversity trade-off.
The Adversarial Robustness of Sampling
Venue: arXiv cs.DS 2019
Year: 2019

Random sampling is a fundamental primitive in modern algorithms, statistics, and machine learning, used as a generic method to obtain a small yet "representative" subset of the data. In this work, we investigate the robustness of sampling against adaptive adversarial attacks in a streaming setting: An adversary sends a stream of elements from a universe $U$ to a sampling algorithm (e.g., Bernoulli sampling or reservoir sampling), with the goal of making the sample "very unrepresentative" of the underlying data stream. The adversary is fully adaptive in the sense that it knows the exact content of the sample at any given point along the stream, and can choose which element to send next accordingly, in an online manner. Well-known results in the static setting indicate that if the full stream is chosen in advance (non-adaptively), then a random sample of size $Ω(d / \varepsilon^2)$ is an $\varepsilon$-approximation of the full data with good probability, where $d$ is the VC-dimension of the underlying set system $(U,R)$. Does this sample size suffice for robustness against an adaptive adversary? The simplistic answer is \emph{negative}: We demonstrate a set system where a constant sample size (corresponding to VC-dimension $1$) suffices in the static setting, yet an adaptive adversary can make the sample very unrepresentative, as long as the sample size is (strongly) sublinear in the stream length, using a simple and easy-to-implement attack. However, this attack is "theoretical only", requiring the set system size to (essentially) be exponential in the stream length. This is not a coincidence: We show that to make Bernoulli or reservoir sampling robust against adaptive adversaries, the modification required is solely to replace the VC-dimension term $d$ in the sample size with the cardinality term $\log |R|$. This nearly matches the bound imposed by the attack.
A Simple Deep Personalized Recommendation System
Venue: arXiv cs.IR 2019
Year: 2019

Recommender systems are critical tools to match listings and travelers in two-sided vacation rental marketplaces. Such systems require high capacity to extract user preferences for items from implicit signals at scale. To learn those preferences, we propose a Simple Deep Personalized Recommendation System to compute travelers' conditional embeddings. Our method combines listing embeddings in a supervised structure to build short-term historical context to personalize recommendations for travelers. Deployed in the production environment, this approach is computationally efficient and scalable, and allows us to capture non-linear dependencies. Our offline evaluation indicates that traveler embeddings created using a Deep Average Network can improve the precision of a downstream conversion prediction model by seven percent, outperforming more complex benchmark methods for online shopping experience personalization.
Interactive Physics-Inspired Traffic Congestion Management
Venue: arXiv cs.MA 2019
Year: 2019

This paper proposes a new physics-based approach to effectively control congestion in a network of interconnected roads (NOIR). The paper integrates mass flow conservation and diffusion-based dynamics to model traffic coordination in a NOIR. The mass conservation law is used to model the traffic density dynamics across the NOIR while the diffusion law is applied to include traffic speed and motion direction into planning. This paper offers an analogy between traffic coordination in a transportation system and heat flux in a thermal system to define a potential filed over the NOIR. The paper also develops an interactive light-based and boundary control to manage traffic congestion through optimizing the traffic signal operations and controlling traffic flows at the NOIR boundary nodes. More specifically, a model predictive boundary control optimizes the NOIR inflow traffic while a receding horizon optimizer assigns the optimal movement phases at the NOIR intersections. For simulation, the paper models traffic congestion in a heterogeneous NOIR with a large number of interior and boundary nodes where the proposed interactive control can successfully manage the congestion.
Deception Strategies and Threats for Online Discussions
Venue: arXiv cs.SI 2019
Year: 2019

Communication plays a major role in social systems. Effective communications, which requires transmission of the messages between individuals without disruptions or noise, can be a powerful tool to deliver intended impact. Language and style of the content can be leveraged to deceive and manipulate recipients. These deception and persuasion strategies can be applied to exert power and amass capital in politics and business. In this work, we provide a modest review of how such deception and persuasion strategies were applied to different communication channels over the years. We provide examples of campaigns that has occurred in different periods over the last 100 years, together with their corresponding dissemination mediums. In the Internet age, we enjoy access to the vast amount of information and the ability to communicate without borders. However, malicious actors work toward abusing online systems to disseminate disinformation, disrupt communication, and manipulate people by the means of automated tools, such as social bots. It is important to study the old practices of persuasion to be able to investigate modern practices and tools. Here we provide a discussion of current threats against society while drawing parallels with the historical practices and the recent research efforts on systems of detection and prevention.
Incentive Mechanisms to Prevent Efficiency Loss of Non-Profit Utilities
Venue: arXiv cs.GT 2019
Year: 2019

The modernization of the power system introduces technologies that may improve the system's efficiency by enhancing the capabilities of users. Despite their potential benefits, such technologies can have a negative impact. This subject has widely analyzed, mostly considering for-profit electric utilities. However, the literature has a gap regarding the impact of new technologies on non-profit utilities. In this work, we quantify the price of anarchy of non-profit utilities, that is, the cost caused by lack of coordination of users. We find that users, in the worst case, can consume up to twice the optimal demand, obtaining a small fraction of the optimal surplus. For this reason, we leverage the theory of mechanism design to design an incentive scheme that reduces the inefficiencies of the system, which preserves the privacy of users. We illustrate with simulations the efficiency loss of the system and show two instances of incentive mechanism that satisfy either budget balance and budget deficit.
Few-Shot Video Classification via Temporal Alignment
Venue: arXiv cs.CV 2019
Year: 2019

There is a growing interest in learning a model which could recognize novel classes with only a few labeled examples. In this paper, we propose Temporal Alignment Module (TAM), a novel few-shot learning framework that can learn to classify a previous unseen video. While most previous works neglect long-term temporal ordering information, our proposed model explicitly leverages the temporal ordering information in video data through temporal alignment. This leads to strong data-efficiency for few-shot learning. In concrete, TAM calculates the distance value of query video with respect to novel class proxies by averaging the per frame distances along its alignment path. We introduce continuous relaxation to TAM so the model can be learned in an end-to-end fashion to directly optimize the few-shot learning objective. We evaluate TAM on two challenging real-world datasets, Kinetics and Something-Something-V2, and show that our model leads to significant improvement of few-shot video classification over a wide range of competitive baselines.
Automatic Coverage Selection for Surface-Based Visual Localization
Venue: arXiv cs.RO 2019
Year: 2019

Localization is a critical capability for robots, drones and autonomous vehicles operating in a wide range of environments. One of the critical considerations for designing, training or calibrating visual localization systems is the coverage of the visual sensors equipped on the platforms. In an aerial context for example, the altitude of the platform and camera field of view plays a critical role in how much of the environment a downward facing camera can perceive at any one time. Furthermore, in other applications, such as on roads or in indoor environments, additional factors such as camera resolution and sensor placement altitude can also affect this coverage. The sensor coverage and the subsequent processing of its data also has significant computational implications. In this paper we present for the first time a set of methods for automatically determining the trade-off between coverage and visual localization performance, enabling the identification of the minimum visual sensor coverage required to obtain optimal localization performance with minimal compute. We develop a localization performance indicator based on the overlapping coefficient, and demonstrate its predictive power for localization performance with a certain sensor coverage. We evaluate our method on several challenging real-world datasets from aerial and ground-based domains, and demonstrate that our method is able to automatically optimize for coverage using a small amount of calibration data. We hope these results will assist in the design of localization systems for future autonomous robot, vehicle and flying systems.
Stepping OCaml
Venue: arXiv cs.PL 2019
Year: 2019

Steppers, which display all the reduction steps of a given program, are a novice-friendly tool for understanding program behavior. Unfortunately, steppers are not as popular as they ought to be; indeed, the tool is only available in the pedagogical languages of the DrRacket programming environment. We present a stepper for a practical fragment of OCaml. Similarly to the DrRacket stepper, we keep track of evaluation contexts in order to reconstruct the whole program at each reduction step. The difference is that we support effectful constructs, such as exception handling and printing primitives, allowing the stepper to assist a wider range of users. In this paper, we describe the implementation of the stepper, share the feedback from our students, and show an attempt at assessing the educational impact of our stepper.
Improved Upper Bounds on the Growth Constants of Polyominoes and Polycubes
Venue: arXiv cs.DM 2019
Year: 2019

A $d$-dimensional polycube is a facet-connected set of cells (cubes) on the $d$-dimensional cubical lattice $\mathbb{Z}^d$. Let $A_d(n)$ denote the number of $d$-dimensional polycubes (distinct up to translations) with $n$ cubes, and $λ_d$ denote the limit of the ratio $A_d(n{+}1)/A_d(n)$ as $n \to \infty$. The exact value of $λ_d$ is still unknown rigorously for any dimension $d \geq 2$; the asymptotics of $λ_d$, as $d \to \infty$, also remained elusive as of today. In this paper, we revisit and extend the approach presented by Klarner and Rivest in 1973 to bound $A_2(n)$ from above. Our contributions are: Using available computing power, we prove that $λ_2 \leq 4.5252$. This is the first improvement of the upper bound on $λ_2$ in almost half a century; We prove that $λ_d \leq (2d-2)e+o(1)$ for any value of $d \geq 2$, using a novel construction of a rational generating function which dominates that of the sequence $\left(A_d(n)\right)$; For $d=3$, this provides a subtantial improvement of the upper bound on $λ_3$ from 12.2071 to 9.8073; However, we implement an iterative process in three dimensions, which improves further the upper bound on $λ_3$to $9.3835$.
Toward Simulating Environments in Reinforcement Learning Based Recommendations
Venue: arXiv cs.IR 2019
Year: 2019

With the recent advances in Reinforcement Learning (RL), there have been tremendous interests in employing RL for recommender systems. However, directly training and evaluating a new RL-based recommendation algorithm needs to collect users' real-time feedback in the real system, which is time and efforts consuming and could negatively impact on users' experiences. Thus, it calls for a user simulator that can mimic real users' behaviors where we can pre-train and evaluate new recommendation algorithms. Simulating users' behaviors in a dynamic system faces immense challenges -- (i) the underlining item distribution is complex, and (ii) historical logs for each user are limited. In this paper, we develop a user simulator base on Generative Adversarial Network (GAN). To be specific, the generator captures the underlining distribution of users' historical logs and generates realistic logs that can be considered as augmentations of real logs; while the discriminator not only distinguishes real and fake logs but also predicts users' behaviors. The experimental results based on real-world e-commerce data demonstrate the effectiveness of the proposed simulator.